TY - GEN
T1 - Feature selection in the analysis of tumor marker data using evolutionary algorithms
AU - Winkler, Stephan M.
AU - Affenzeller, Michael
AU - Kronberger, Gabriel
AU - Kommenda, Michael
AU - Wagner, Stefan
AU - Jacak, Witold
AU - Stekel, Herbert
PY - 2010
Y1 - 2010
N2 - In this paper we describe the use of evolutionary algorithms for the selection of relevant features in the context of tumor marker modeling. Our aim is to identify mathematical models for classifying tumor marker values AFP and CA 15-3 using available patient parameters; data provided by the General Hospital Linz are used. The use of evolutionary algorithms for finding optimal sets of variables is discussed; we also define fitness functions that can be used for evaluating feature sets taking into account the number of selected features as well as the resulting classification accuracies. In the empirical section of this paper we document results achieved using an evolution strategy in combination with several machine learning algorithms (linear regression, k-nearest-neighbor modeling, and artificial neural networks) which are applied using cross-validation for evaluating sets of selected features. The identified sets of relevant variables as well as achieved classification rates are compared.
AB - In this paper we describe the use of evolutionary algorithms for the selection of relevant features in the context of tumor marker modeling. Our aim is to identify mathematical models for classifying tumor marker values AFP and CA 15-3 using available patient parameters; data provided by the General Hospital Linz are used. The use of evolutionary algorithms for finding optimal sets of variables is discussed; we also define fitness functions that can be used for evaluating feature sets taking into account the number of selected features as well as the resulting classification accuracies. In the empirical section of this paper we document results achieved using an evolution strategy in combination with several machine learning algorithms (linear regression, k-nearest-neighbor modeling, and artificial neural networks) which are applied using cross-validation for evaluating sets of selected features. The identified sets of relevant variables as well as achieved classification rates are compared.
KW - Classification
KW - Data mining
KW - Evolutionary algorithms
KW - Machine learning
KW - Medical data analysis
KW - Statistical analysis
KW - Tumor marker modeling
UR - http://www.scopus.com/inward/record.url?scp=84856905810&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84856905810
SN - 2952474788
SN - 9782952474788
T3 - 22th European Modeling and Simulation Symposium, EMSS 2010
SP - 1
EP - 6
BT - 22th European Modeling and Simulation Symposium, EMSS 2010
T2 - 22th European Modeling and Simulation Symposium, EMSS 2010
Y2 - 13 October 2010 through 15 October 2010
ER -